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Complexity theory: dynamics and non-linearity are the onlyreason for knowledge management to exist

Prof dr Walter Baets

Euromed center for Knowledge Management (EcKM)

Euromed Marseille–

Ecole de Management (F) and Nyenrode University (NL)

E-mail:walter.baets@euromed-marseille.com

1. Introduction

A lot has been said and written about knowledge management, probably startingwith the proponents of the learning organization on the one hand, and Nonaka’s viewon knowledge management on the other hand.

Increasingly, authors have added thesubject to their vocabulary and the more that the ‘general management thinkers’have got involved (Leonard-Barton, Drucker, etc.) the more knowledge managementhas acquired the status of a major buzzword. In the 1999

European Conference onInformation Systems (Copenhagen) the ‘best research paper award’ was given to apaper that argued that knowledge management would be the next hype to forgetpeople (Swan et al., 1999). This choice appeared to me to represent a public act ofmasochism on behalf of the IS community, given that IS experts, more than anyother people, should have a clear idea of why knowledge management is here tostay.

This chapter attempts to provide a broad framework for the subject, highlightingthe different aspects (including the human ones) which should be considered whentalking about knowledge management. This ‘taxonomy in brief’ is of course based ona particular paradigm (as any other taxonomy) that is known as the complexityparadigm. Looking through the lenses of complexity theory, we can see whyknowledge management is a new and fundamental corporate activity. Complexitytheory allows us to understand why knowledge is a corporate asset and why and howit should be managed. The lensesof complexity theory allow us to say thatknowledge management is not just another activity of importance for a company.

A number of knowledge management projects, based on this taxonomy, wereresearched over the last 5 years within Notion (The Nyenrode Institute forKnowledge Management and Virtual Education), a research center fully sponsoredby Achmea (second largest Dutch insurance holding; the fifth largest within its

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European network), Atos/Origin, Philips, Sara Lee/DE and Microsoft. Full detail ofthose research projects can be found in Baets (2004a)

This chapter attempts to present the complete picture of KM, starting with theparadigm, covering the infrastructure and process, with the aim of clarifying thesubject of study. Both the corporate and

the academic perspectives appear in thispaper.

2. The knowledge era

An important and remarkable evolution in what we still call today the industrialworld is that it is no longer industrial. We witness a rapid transition from anindustrial society into a knowledge society. The knowledge society is based on thegrowing importance of knowledge as the so-called fourth production factor. Manyproducts and certainly all services have a high research and development cost,whereas the production cost itself is rather low. Developing and launching a newoperating system like Windows costs a huge amount of investment for Microsoft,which makes the first copy very expensive, but any further copies have a very lowproduction cost. Having a number of consultants working for a company is a largeinvestment for a consulting company, so when they are actively working on aproject, their marginal cost is close to zero. Having the knowledge base, whichmeans having the consultants available is expensive. Their real work for a client isrelatively cheaper. Even the best example of industrial production in the Westernpart of the world, which is car manufacturing, became increasingly knowledgebased. More than 40% of the sales price of a car is due to research, developmentand marketing.

We still talk about the industrialized countries, since most of our thinking is stillbased on concepts of industrial production dating back to the earlier parts of theprevious century (the 20th, if not even the end of the 19th

century). What we haveobserved, though, is that increasingly companies get involved in optimizing supplychains and that those supply chains evolve into demand and supply chains. Thefollowing step consists of supporting those chains with information technology (IT)in order to increase efficiency. The strange thing that happens in a next stage isthat a progressive use of IT puts pressure on the existence itself of the chain.The better a chain is integrated based on IT, the more a pressure gets createdwhich makes the chain explode into a network. Particularly in such circumstances,the ‘owner’ of the knowledge base manages the process. Network structures evolvearound knowledge centers. Companies manage brands and outsource most of thechain itself. Extreme examples of this approach are probably Calvin Klein,Benetton and Nike. Again, knowledge and particularly the capacity to manage,

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create and share knowledge is becoming the center of the scope of the successfulcompany. This can be translatedvia brand management, direct marketing totargeted clients, etc. but it is the visual part of the evolution from an industrialmarket into a knowledge based market. Knowledge becomes yet another attributeof the changing economic reality.

Knowledge in acompany has different forms and most commonly one regroups theseforms into three categories of knowledge. Tacit knowledge is mainly based on livedexperiences while explicit knowledge refers to the rules and procedures that acompany follows. Cultural knowledge then is the environment in which the companyand the individual (within the company) operate.

Different forms of knowledge are crafted by various different activities.Conversion of knowledge takes place based on the tacit and explicit knowledge

thata person possesses or has access to. The creation of knowledge very often takesplace during joint work sessions, such as brainstorms, management meetings, etc.Equally important but more difficult to capture is knowledge processing viaassimilation. Very often, assimilation is based on cultural knowledge as a firstinput, reinforced with tacit knowledge that quit often collapses with explicit rulesand regulations. It seems important to stress, however, that knowledgemanagement is only the ‘sufficient’ condition. The ‘necessary’ condition in order todeal with new economic realities is the boundary condition for knowledgemanagement and that is the learning culture of the company. On top of the merefact that the most interesting knowledge is implicit and therefor ‘stored’ in people,it is the dynamics of the knowledge creation and sharing activity (let us foreasiness call this learning) where the people come a second time in the picture.

Above all, knowledge management and learning is an attitude and a way of workingwith management. It is an overall approach that goes beyond the addition of anumber of functional tactics. One could even say that it is a kind of philosophy ofmanagement, rather than a science. This process is one of redefining the target ofthe company from a profit making or share-value increasing entity to a knowledge-creating and sharing unit. The first type of organization has a rather short-termfocus, whereas the latter type has a more visionary and long-term one.

The aim of the company is no longer purely growth as such, but rather it becomessustainable development and renewal. Hence, organizations not only needknowledge,

they also need the skills and competencies to dynamically update and put

knowledge into practice. This results in the need for organizations to learncontinuously and to look for continuous improvement

A learning organization enables each of its members to continually learn and helpsto generate new ideas and thinking. By this process, organizations continuouslylearn from their own and others experience, adapt and improve their efficiencytowards the achievement of their goal. In a way, learning organizations aim toconvert themselves into "knowledge-based" organizations by creating, acquiring andtransferring knowledge so as to improve their planning and actions.

In order to build a learning organization, or a corporate learning culture, companiesshould be skilled at systematic problem solving, learning from their own experience,learning from the experiences of others, processing knowledge quickly andefficiently through the organization and experimenting with new approaches.Developments in information and knowledge technologies make it increasinglypossible to achieve these competitive needs and skills.

3. The complexity paradigm

In the past, identifiable when market change moved slower, we got used to thinkingin terms of reasonably linear behavior as markets and industries appeared to bemore stable or mature. Concretely, one thought one could easily forecast futurebehavior based on past observations and in many respects we developed complex(and sometimes complicated) methods to extrapolate linear trends (Prigogine andStengers, 1988; Nicolis and Prigogine, 1989). But in reality, markets do and did notbehave in a linear way. The future is not

a simple extrapolation of the past. Agiven action can lead to several possible outcomes ("futures"), some of which aredisproportionate in size to the action itself. The "whole" is therefore not equal tothe sum of the "parts". This contrasting perspective evolved from complexity andchaos theory. Complexity theory challenges the traditional managementassumptions by embracing non-linear and dynamic behavior of systems, and bynoting that human activity allows for the possibility of emergent behavior(Maturana and Varela, 1984). Emergence can be defined as the overall systembehavior that stems from the interaction of many participants-

behavior thatcannot be predicted or even "envisioned" from the knowledge of what eachcomponent of a system does.

Organizations, for example, often experience changeprocesses as emergent behavior. Complexity theory also tells corporate executivesthat beyond a certain point, increased knowledge of complex, dynamic systems doeslittle to improve the ability to extend the horizon of predictability for thosesystems. No matter how much one knows about the weather, no matter howpowerful the computers, specific long-range predictions are not possible. Knowing

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is important, not predicting, thus there is no certainty (Stewart, 1989; Cohen andStewart, 1994)).

The focus on non-linear behavior of markets collides with the traditional positivistand Cartesian view of the world. That positivist perspective translated in thetraditional management literature-

the stuff that most MBAs are taught-

describes "the" world in terms of variables and matrices, and within a certainsystem of coordinates. Exact and objective numbers are needed in order to createmodels while simulations can offer a ‘correct’ picture of what to expect.Particularly business schools have welcomed this ‘scientific’ way of dealing withmanagement problems as the one which could bring business schools up to the"scientific" level of the beta sciences. It is clear that much of the existingmanagement practice, theory, and "remedies" based on the positivist view arelimited by their dependence on several inappropriate assumptions as they don'treflect business and market behavior. Linear and static methods are the ones thatare taught in business schools. Therefore, markets have to be linear and static.As we know they are not (Arthur, 1990).

It seems important to elaborate a little more on positivist thinking as we want topropose later a different paradigm.

A major aspect of positivism is the division between object and subject. Thismeans that the outer world (e.g. an industry) is pre-given, ready to be "truthfully"represented by organizations and individuals. The mind is able to create an innerrepresentation that corresponds to the outer world, be it an object, event or state.Translated to knowledge, positivism considers that knowledge exists independentof the human being that uses it, learns it, transfers it. Knowledge reflects andrepresents “the world in itself” and can be built up independent of the observer,the “knower”. What if the universal knowledge that is transferred is mainly atheoretical framework, a form which is of little use in the non-linear and dynamicmarkets?

Another premise of positivist thinking is based on a strict belief in (absolute)causality and (environmental) determinism. As there exist clear-cut connectionsbetween cause and effect, managerial actions lead to predictable outcomes andthus to control. Successful systems are driven by negative feedback processestoward predictable states of adaptation to the environment. The dynamics ofsuccess are therefore assumed to be a tendency towards stability, regularity, andpredictability. The classic approach to strategy illustrates this reductionism. Thecomplexities of industries are reduced in terms of maturity, continuity andstability so that a single prediction of an organization's future path can bedescribed. As a consequence, the better the environmental analysis according to a

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number of dimensions, the better the course (strategy) can be defined andimplemented (Baets and Van der Linden, 2000, 2003).

My own research over the last years, and currently undertaken in the EcKM,suggests that instead of searching for causality, the concept of synchronicity(being together in time), often referred to as a quantumstructure, allows muchmore insight in business dynamics (Baets, 2004b). Indeed that quantumstructureis a holistic concept of management, based on interacting “agents”. Those networksof agents/people create emergent behavior and knowledge.

Positivism is the prevailing scientific view in the Western world, since it perfectlycoincides with the Cartesian view of the world: the over-riding power of man as afact of nature. Nature gives man the power to master nature, according to laws ofnature. In 1903 however, Poincaré, a French mathematician, brought some doubt inthis positivist view. Without really being able to prove, or even to gather evidence,he warned:

"Sometimes small differences in the initial conditions generate verylarge differences in the final phenomena. A slight error in theformer could produce a tremendous error in the latter. Predictionbecomes impossible; we have accidental phenomena."

It suggested that with the approaches used, man was not always able to controltheir own systems. Hence, there's the limit to the Cartesian view of the world.

It took quite a number of years until, in 1964, Lorenz showed evidence of thephenomenon. Lorenz, an American meteorologist, was interested in weatherforecasting. In order to produce forecasts, he built a simple dynamic non-linearmodel. Though it only consisted of a few equations and a few variables, it showed"strange" behavior. A dynamic model is one where the value in a given

period is afunction of the value in the previous period. For example, the value of a particularprice in a given period is a function of its value in the previous period. Or, themarket share for product A in a given period is a function of the marketshare inthe previous period. In other words, most if not all, economic phenomena aredynamic. Such a dynamic process that continuously changes can only be simulatedby a stepwise procedure of very small increments. It is an iterative process. Oncethe

value of the previous period is calculated, it is used as an input value for thenext period, etc.

A computer allowed Lorenz to show what could happen with non-linear dynamicsystems. As is known, he observed that very small differences in starting values

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caused chaotic behavior after a number of iterations. The observed differencebecame larger than the signal itself. Hence, the predictive value of the modelbecame zero (Stewart, 1989). Lorenz's observation caused a real paradigm shift insciences. Lorenz showed what Poincaré suggested, namely that non-linear dynamicsystems are highly sensitive to initial conditions. Complex adaptive systems areprobabilistic rather than deterministic, and factors such as non-linearity canmagnify apparently insignificant differences in initial conditions into hugeconsequences, meaning that the long term outcomes for complex systems areunknowable. Today we know, thanks to the integration of ideas of the two mainscientific revolutions of the last century (relativity and quantummechanics), thatanother underlying problem, aggravating the complex structure, is the structure ofsynchronicity in the “business nature”.

Translated to management, this advocates that companies and economies need tobe structured to encourage

an approach that embraces flux and competition incomplex and chaotic contexts rather than a rational one. Mainstream approachespopularized in business texts, however, seldom come to grips with non-linearphenomena. Instead, they tend to model phenomena as if they were linear in orderto make them tractable and controllable, and tend to model aggregate behavior asif it is produced by individual entities which all exhibit average behavior.

Positive feedback has been brought into the realm of economics by Brian Arthur(Arthur, 1990), who claims that there are really 2 economies, one that functions onthe basis of traditional diminishing returns, and one where increasing returns toscale are evident due to positive feedback. Marshall introduced the concept ofdiminishing returns already in 1890. This theory was based on industrialproduction, where one could chose out of many resources and relatively littleknowledge was involved in production. Production then seemed to follow the law ofdiminishingreturns, based on negative feedback in the process and this led to aunique (market) equilibrium. Arthur's second economy includes most knowledgeindustries. In the knowledge economy, companies should focus on adapting,recognizing patterns, and building

webs to amplify positive feedback rather thantrying to achieve "optimal" performance. A good example is VHS becoming amarket standard, without being technically superior. A snowball effect ensuedwhich made VHS the market standard, even though Betamax offered bettertechnology at a comparable price.

Arthur also specified a number of reasons for increasing returns that particularlyfits today's economy. Most products, being highly knowledge intensive, with highup-front costs, network effects, and customer relationships, lead to complexbehavior. Let us take the example of Windows. The first copy of Windows is quiteexpensive due to huge research costs. Microsoft experiences a loss on the first

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generation. The second and following generations cost

very little comparatively,but the revenue per product remains the same. Hence, there is a process ofincreasing returns.

Two more interesting developments have consequences for our argument. Recentneurobiological research, e.g. by Varela (Maturana and Varela, 1984), has revealedthe concept of self-organization and the concept that knowledge is not stored, butrather created each time over and again, based on the neural capacity of the brain.Cognition is enacted, which means that cognition only exists in action andinterpretation. This concept of enacted cognition goes fundamentally against theprevailing idea that things are outside and the brain is inside the person. Thesubject can be considered as the special experience of oneself, as a processinterms of truth. By identifying with objects, the individual leaves the opportunityfor the objects to "talk". In other words, subject and object meet in interaction,in hybrid structures. Individuals thus become builders of facts in constructingcontents of knowledge which relate to events, occurrences and states. Knowledgeis concerned with the way one learns to fix the flow of the world in temporal andspatial terms. Consequently, claims of truth are transposed on objects; the subjectis "de-subjectivised". There is not such subdivision between the object and thesubject. Cognition is produced by an embodied mind, a mind that is part of a body,sensors and an environment (Baets, 1999; Baets and Van der Linden, 2000).

Research in artificial life gave us the insight that instead of reducing the complexworld to simple simulation models, which are never correct, one could equally definesome simple rules, which then produce complex behavior (Langton, 1989). This isalso a form of self-organization,like the flock of birds that flies south. The firstbird is not the leader and does not command the flog. Rather, each bird has asimple rule e.g. to stay 20 cm away from its two neighbors. This simple rule allowsus to simulate the complex behavior ofa flock of birds.

Probabilistic, non-linear dynamic systems are still considered deterministic. Thatmeans that such systems follow rules, even if they are difficult to identify andeven if the appearance of the simulated phenomenon suggests complete chaos. Thesame complex system can produce at different times, chaotic or orderly behavior.The change between chaos and order cannot be forecast, nor can the moment inwhich it takes places, either in magnitude or direction. Complexity and chaos refertothe state of a system and not to what we commonly know as complicated, i.e.something that is difficult to do. The latter depends not on the system, but moreon the environment and boundary conditions. Perhaps for a handicapped person,driving a car is more complicated. In general, building a house seems morecomplicated than sewing a suit, but for some other people building a house would be

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less complicated than sewing a suit. This depends on the boundary conditions foreach individual person.

To formalize in a simplified way the findings of complexity theory, we could statethree characteristics. First, complex systems are highly dependent on the initialstate. A slight change in the starting situation can have dramatic consequences in alater period of time caused by the dynamic and iterative character of the system.Second, one cannot forecast the future based on the past. Based on theirreversibility of time principle (of Prigogine), one can only make one step ahead ata time, scanning carefully

the new starting position. Third, the scaling factor of anon-linear system causes the appearance of "strange attractors", a local minimumor maximum around which a system seems to stay for a certain period of time inquasi equilibrium. The number of attractors cannot be forecasted, neither can itbe forecasted when they attract the phenomenon.

There are a myriad of insights we gain from complexity theory and its applicationsin business and markets for knowledge management (Baets, 1998; Baets and Vander Linden, 2000).

The ‘irreversibility of time’ theorem suggests that there is no best solution. Thereare "best" principles of which one can learn, but no best solutions or practices thatone could copy. There are even no guaranteed solutions that could be used in mostcircumstances. This fact deems the need for a different way of organizing theprocess of knowledge creation and knowledge management.

4. What should be understood by Knowledge Management: the corporateview

Allow recalling that this

chapter attempts to present the complete picture ofknowledge management, starting from the paradigm, covering the infrastructureand process, with the aim of clarifying the subject of study. Though the corporateand the academic perspectives are at times a little different, they both appear infigure 1.

Any managerial concept is based on a particular paradigm and according to the viewdeveloped in this paper, the paradigm of

complexity (non-linear and dynamicsystems behavior) sheds interesting and refreshing light on the nature ofknowledge management. Earlier in this chapter we have explained why thecomplexity paradigm positions knowledge at the center of a knowledge-basedcompany and it does so increasingly with virtual or extended companies.

The left side of the figure shows the corporate logic in understanding knowledgemanagement. The paradigm serves as the glasses through which we look to thecorporate purpose (gaining sustainable competitive advantage, or expressedsimpler, survival) and what we observe then is the means to achieve the purpose,i.e. asset management. The chosen glasses allow to identify (observe) the wayahead in reaching the goal. The immediate‘next’ step is the ‘infrastructure’ orstakeholders necessary for knowledge management:

The corporate purpose remains to create sustainable competitive advantage, andthe means for realizing that is (and has always been) asset management. However,for knowledge intensive companies this means that knowledge management movesinto the picture. A translation (a filter) above and beyond the necessaryintegration of infrastructure and stakeholders is necessary in order to combine the

infrastructure in knowledge management. That filter is a dynamic process, in whichthe ‘learner’ should be given responsibility. Pedagogical metaphors give us aninsight into this filter process (Baets, 1999).

The prevailing pedagogical metaphor is the transfer metaphor. Knowledge ingeneral and, more specifically, subject matters, are viewed as transferablecommodities. A student (a learner) is seen as a vessel positioned alongside aloading dock. ‘Knowledge’ is poured into the vessel until it is full. Whereas thestudent is the empty vessel, the teacher is a crane or a forklift. The teacherdelivers and places knowledge into the empty vessel. Courses applying the transfertheory would be very much lecture-based, would include talks from leading figuresin the relevant fields (the more the better) and would provide students withduplicated course notes. Once the vessel is filled, a ‘bill of loading’, which is thediploma, certifies the content of the vessel. IT improves the speed of the loading(with high tech cranes). Nobody can guarantee that in the next harbor, the cargois not taken out of the ship. Monitoring a student means monitoring the process offilling the vessel and sometimes sampling the quality of the contents. This samemetaphor became the prevailing one while talking about (virtual) knowledgemanagement approaches (Baets andVan der Linden, 2000; 2003).

However, since knowledge appears to be dynamic and learning non-linear (based onour paradigm), another paradigm is necessary. Here again educational scienceprovides us with a valid illustration. The travelling metaphor isone by which theteacher initiates and guides the students through an unknown terrain that needs tobe explored. The student is the explorer and the teacher/tutor is the experiencedand expert travelling companion and counselor. The guide not only points

out theway, but also provides travelling maps and a compass. The ‘teaching methods’ (if onecan still call them such) which are most used in applying this theory are experientialmethods: simulations, projects, exercises with unpredictable outcomes (as in somecase studies), discussions and independent learning. In courses applying thistheory, monitoring means regularly comparing each other’s travelling notes.Experiments have shown that this theory is particularly effective in adulteducation, since adults are better equipped in order to deal with the increasedresponsible that the ‘learner’ has in this paradigm. One step on from the travellingtheory is the growing metaphor. In many respects, this theory does not differgreatly from the previous one.

Rather, it is an extension of it, which focuses more

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on the self-initiative of the student. Subject matters are a set of experiencesthat each student should incorporate into his/her personality. The aim for thestudent is to develop his/her personality. This latter paradigm (be it the travellingmetaphor or the growing one) perfectly fits complexity theory (our overallparadigm or glasses). It allows us to integrate the infrastructure into assetmanagement. It introduces the rational for work placelearning, and the necessaryintegration of the latter with knowledge management. This makes knowledgemanagement different from and value adding to information management.

5. Research perspective on Knowledge Management

The combination of infrastructure (with its different stakeholders and/ordisciplines) and the learning process (filter) makes knowledge management what itshould be. Most existing knowledge management theories either do not get muchfurther than a discussion of means and purposes, orthey overstress one of theinfrastructural aspects, ignoring the unity and necessity of all the three elementstogether. In our view, knowledge management, knowledge creation and knowledgesharing (via virtual learning platforms) are integral parts of the

same model.

From a research perspective, we consider complexity theory as the basic science(s)involved. In particular the following concepts are of importance for the correctunderstanding of the paradigm and its consequences for knowledge management:

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Sensitivity of the complex system to initial conditions

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Existence of (many) strange attractors in complex systems

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Irreversibility of time principle (Prigogine)

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Behavior of complex systems far away from equilibrium (Prigogine)

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Learning behavior of systems

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Autopoeisis (Varela)

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Embodied mind (Varela)

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Enacted cognition (Varela)

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Artificial life research and its applications (Langton)

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Law of increasing returns (Arthur)

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Quantumstructure of business

All these aspects need a good explanation and a clear link to managerialconsequences is necessary.

As already mentioned earlier, and visualized in figure 1, the disciplines involved inknowledge management are human resources management and management

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development, ICT and particularly artificial intelligence (AI), and businesseducation, increasingly virtual education. The management development functionshould be the driver in this knowledge creation, knowledge sharing, learningprocess, ensuring that each individual receives at the pace that s/he can process.MD should provide further the learning conditions. It is unavoidable that ICT andAI are necessary in order to support the knowledge management process (Baetsand Venugopal, 1998; Venugopal and Baets, 1995). Building IT platforms, extractingknowledge via AI and virtual education are only some of the aspects where IT is ofhelp. Business education, and increasingly this includes virtual education, isresponsible for creating some input in the learning process but equally to makesome of the extracted knowledge accessible for each individual. Businesseducation in this respect has also to do with the content. The aspect of knowledgesharing is an educational one too. Knowledge management, therefore, needs tointegrate successfully disciplines like human resources management, organizationalsciences, educational sciences, artificial intelligence and cognitive sciences, etc,implicitly defining a knowledge management research agenda.

It is my firm belief that in the decade to come, we will see a breakthroughin theunderstanding of the underlying theory justifying the (corporate) necessity forknowledge management, in line with the agenda set out in this chapter. Assuggested earlier, the consequence of the concepts developed here and its logicalextension is

an unavoidable ontological discussion about causality versussynchronicity. In my work (2004b) I call this the quantum structure of business(or in particular, in the reference, of innovation), which provides an integrated andapplicable theoretical andconceptual framework in order to understand and manageconsequently dynamic processes, knowledge management only being one of them.The first research projects undertaken confirm this potential understanding andits application in business. It is the acceptance of the ontological evidence forsynchronicity that drives the research agenda of EcKM.